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Generalized Low-Rank Approximations

Author(s)
Srebro, Nathan; Jaakkola, Tommi
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Abstract
We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving {\\em weighted} low rank approximation problems, which, unlike simple matrix factorization problems, do not admit a closed form solution in general. We analyze, in addition, the nature of locally optimal solutions that arise in this context, demonstrate the utility of accommodating the weights in reconstructing the underlying low rank representation, and extend the formulation to non-Gaussian noise models such as classification (collaborative filtering).
Date issued
2003-01-15
URI
http://hdl.handle.net/1721.1/6708
Other identifiers
AIM-2003-001
Series/Report no.
AIM-2003-001
Keywords
AI, svd pca

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